增强高速公路交通状态感知：一种新的基于张量完成的BAS优化PSO-BP融合模型
Enhancing Expressway Traffic State Perception: A Novel BAS-Optimized PSO-BP Fusion Model with Tensor Completion.
摘要(中文译文)
随着高速公路网络的不断扩展和交通需求的快速增长，传统单一来源交通检测数据在时空覆盖范围和精度方面存在局限，难以支撑智能高速公路的精细化运营与管理。现有数据预处理方法往往无法充分捕捉全局时空特征，传统PSO-BP神经网络易陷入局部最优。针对上述问题，本研究利用G50沪渝高速江苏段ETC-DSRC与RTMS微波数据开展多源交通数据融合研究。采用HaLRTC张量补全算法对缺失和异常数据进行修复，充分挖掘交通流的时空相关性特征。将天牛须搜索（BAS）机制引入粒子群优化（PSO）过程，以改善粒子搜索行为和种群多样性。在此基础上，构建了经BAS优化的PSO-BP神经网络（本研究称为BSO-BP）用于多源交通数据融合。该模型利用改进的PSO算法优化反向传播（BP）神经网络的初始权重和阈值，从而提高融合模型的全局搜索能力和收敛稳定性。以路段平均速度为融合目标，采用MAE、RMSE和MAPE进行精度验证。结果表明，所提模型精度显著高于单源数据方法及BP、PSO-BP和GA-PSO-BP模型，能够更准确地反映路段真实交通状态。
Abstract
With the continuous expansion of the expressway network and the rapid growth of traffic demand, traditional single-source traffic detection data is limited in spatial-temporal coverage and accuracy, which can hardly support the refined operation and management of intelligent expressways. Existing data preprocessing methods often fail to fully capture global spatiotemporal features, and traditional PSO-BP neural networks are prone to local optima. To address these issues, this study investigates multi-source traffic data fusion using ETC-DSRC and RTMS microwave data from the Jiangsu section of the G50 Shanghai-Chongqing Expressway. The HaLRTC tensor completion algorithm is adopted to repair missing and abnormal data, fully mining the spatial-temporal correlation characteristics of traffic flow. The beetle antennae search (BAS) mechanism is introduced into the particle swarm optimization (PSO) process to improve particle search behavior and population diversity. On this basis, a BAS-optimized PSO-BP neural network, referred to as BSO-BP in this study, is constructed for multi-source traffic data fusion. In this model, the improved PSO algorithm is used to optimize the initial weights and thresholds of the backpropagation (BP) neural network, thereby improving the global search capability and convergence stability of the fusion model. Taking the average road speed as the fusion target, MAE, RMSE and MAPE are used for accuracy verification. The results show that the proposed model has significantly higher accuracy than single-source data methods and BP, PSO-BP, and GA-PSO-BP models, and can reflect the real traffic state of road sections more accurately.
如何引用
AMA
Jiacheng Yin, Xiaofei Guo, Wei Bai, Lijing Ma, Li Tang. Enhancing Expressway Traffic State Perception: A Novel BAS-Optimized PSO-BP Fusion Model with Tensor Completion.. Sensors (Basel, Switzerland). 2026; doi:10.3390/s26102998.
APA
Jiacheng Yin, Xiaofei Guo, Wei Bai, Lijing Ma, Li Tang (2026). Enhancing Expressway Traffic State Perception: A Novel BAS-Optimized PSO-BP Fusion Model with Tensor Completion.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26102998
GB/T 7714
Jiacheng Yin, Xiaofei Guo, Wei Bai, Lijing Ma, Li Tang. Enhancing Expressway Traffic State Perception: A Novel BAS-Optimized PSO-BP Fusion Model with Tensor Completion.[J]. Sensors (Basel, Switzerland), 2026 doi:10.3390/s26102998.
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